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\chapter{Literature Review}
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The uses of collaboration have not only been anticipated but rigorously researched upon. \citep{c1} states that collaboration is a widely utilized strategy for addressing complex social issues and for facilitating organizational innovation and performance. The paper lays emphasis on unique positioning of evaluators to empirically examine the development and effects of interagency and interprofessional collaboration. The authors present the Collaboration Evaluation and Improvement Framework (CEIF) which identifies five points of entry to evaluating collaborations and suggests actions that evaluators can take to (a) define and describe the evaluand of collaboration, (b) measure the attributes of organizational collaboration over time, and (c) increase stakeholder capacity to engage in efficient and effective collaborative practices. On a extended note, \citep{c2} recognizes collaboration as an effective means to address multifaceted community issues but states that successful collaboration is difficult to achieve and failure is prevalent. To effectively collaborate, collaborators must recognize the strengths and weaknesses within their own efforts. The paper presents a a seven-factor model of effective collaboration is presented along with an accompanying evaluation tool, the Collaboration Assessment Tool (CAT). As evaluators are increasingly asked to evaluate collaborations and coalitions, this conceptual model and tool can provide evaluators with a grounded, reliable, and valid assessment instrument to work with clients to build collaborative efforts in an intentional, comprehensive, and effective manner. \\

A recent study by \citep{c4} in health sector in Africa tried to use collaboration and partnership as an effective means of regulation and practice of nursing and midwifery. In this study, it was proposed that interprofessional teamwork among stakeholders at global, regional, national and grass-root levels is required to successfully implement global health strategies. Collaboration is also being widely used to escalate the level of education and research. In her latest study, \citep{c5} talks about two problems - ``How can we efficiently design graduate students’ major course learning outcomes into a project-based learning environment?" and ``How can we have graduate students collectively contribute their cross-collaborative student learning outcomes in an efficient protocol?" The technique used is called - ``Lives in Context" and guides the collaborative cross-course BlackBoard shell design. Collective Decision Making is not limited to Humans. A recent research by \cite{c6} shows how ants collectively responds to seasonal changes. The ability of animals to adjust their behaviour according to seasonal changes in their ecology is crucial for their fitness. The authors show that nest preference by emigrating Temnothorax albipennis ant colonies is influenced by a season-specific modulatory pheromone that may help tune decision-making according to seasonal constraints. \\

Wisdom of crowds or collective wisdom is another form of collaboration. It has proven to be an effective method for forming accurate judgments in situations of uncertainity. The essence of the idea
is simple: by collecting a large number of judgements and combining those judgements into a single, collective judgement, one will obtain a judgement that will tend to be more accurate than the individual judgements. In the language of folk wisdom: two heads are often better than one, and more heads tend to be even better. \citep{wc1} talks about using the wisdom of crowds to do a meta-analysis study of the relative accuracies for a range of methods of aggregating confidence interval estimates of unknown quantities. The authors found that a simple trim-and-average method - that is, remove outliers and then average - produced the most accurate estimate. The phenomenon of wisdom of crowds is responsible for the largely loved and used crowdsourcing which has gained immense popularity in recent times. With applications like True Caller in practice, a greater future is being seen in this field. Wikipedia, itself, is one of the biggest examples of crowdsourcing. This approach has shown promise with respect to the task of accurately forecasting future events. Research has demonstrated the value of utilizing meta-forecasts (forecasts about what others in the group will predict) when aggregating group predictions. In their paper, \citep{wc2} describe an extension to meta-forecasting and demonstrate the value of modeling the familiarity among a population's members (its social network) and applying this model to forecast aggregation. A pair of studies demonstrates the value of taking this model into account, and the described technique produces aggregate forecasts for future events that are significantly better than the standard Wisdom of Crowds approach as well as previous meta-forecasting techniques. \\

Seeing the importance and usage of Collaboration in various fields, it is important to predict Collaboration. An attempt was made by \citep{c3} to predict early Collaboration related to Health Advocacy. The authors apply social network analysis (SNA) to examine how organizational characteristics and interorganizational relationships related to early collaboration on advocacy activities within advocacy coalitions. Their findings suggest that even organizations that have not worked together before can become engaged in collaborative activities at a relatively early stage. \\


Wikipedia, being the largest online free encyclopedia, has gained the interest of many researchers. \citep{p1}, \citep{p2} and \citep{p3} describe Wikipedia as a prominent example of community-based production model. A recent study by \citep{wiki2} used Wikipedia to estimate the prevalence of Influenza-like Illness (ILI) in the United States. The authors monitored the number of views on a particular page in a given period and estimated the total number of cases of influenza cases in US. Wikipedia-derived ILI (models performed well through both abnormally high media coverage events (such as during the 2009 H1N1 pandemic) as well as unusually severe influenza seasons (such as the 2012–2013 influenza season). \\


Apart from the effect of Wikipedia on Society, it is important to understand the growth in the articles. Clearly, lack of traditional monetary incentives in Wikipedia raises a big question on its preserverance and growth. However, \citep{incentive} takes into account human nature and define some incentives such as anticipated reciprocity, sense of efficacy and attachment/commitment to the online community. \citep{p3} also talks about several human traits that motivates people to contribute to Wikipedia. Loose governance, open accessibity  and absence of tradition control measures raises a lot of concern about the quality of information in the online encycolpedia. Recent studies like \citep{p4} have shown that quality of Wikipedia is comparable to traditional encyclopedia. Wikipedia was called by \citep{everything} as 'Encyclopedia of Everything' whereas \citep{zech} uses it to extract lexical semantic knowledge. \citep{p7, p8} talks about various scope and coverage in Wikipedia articles while \citep{p6} gives a metric to measure quality. \\

A few studies throw light on the various factors that are related to Wikipedia articles quality: \citep{wiki1}  found that individual contributions quality is completely linked with the number of contributions, and that contributions of highest quality comes from a large number of anonymous, infrequent editors.
As stated, quality of an article greatly depends on the people who write it. \citep{Hu} talks about the interdependency on quality of an article on quality of a reviewer and provides a measure for it. It studies models for automatically deriving Wikipedia article quality rankings based on the interaction data between articles and their contributors. It associates each word in the article by number of reviews it has passed and quantify the article quality as sum of the scores of all the words. The quality of reviewer is then calculated from the quality of the article calculated above. \citep{suzuki} also talks about calculating quality score on a Wikipedia article by mutually evaluating text and editors. \citep{arazy} talks about how quality of an article is affected by inequality in contribution by local and global editors. For the articles studied, the average number of editors was found to be $249$ while the standard deviation was about 777.\\

\citep{marco} states the need for an automatic quality check on Wikipedia Article. It explores a significant number of quality indicators and study their capability to assess the quality of Wikipedia Articles. Further, machine learning techniques were explored to combine these quality indicators into one single assessment judgment.\\

In \citep{wohner}, Wohner and Peter talk about Web2.0, calling Wikipedia to be the most popular application. They measure quality of Wikipedia in terms of ``persistent" and ``transient" contribution. Persistent contributions refer to those edits in Wikipedia which retain for a long period. They conclude that articles with higher persistent contributions tend to have better quality. The study also investigated the automatic quality assessment of Wikipedia articles and concluded that wikis are appropriate choice for such automation owing to the availability of version history.\\

{\citep{wikiQuality}}, itself, has employed methods to measure quality. The articles in Wikipedia are divided into the following categories:
\begin{itemize}
\item Featured Article: Best Wikipedia Articles
\item A-Class: Complete Articles with few pending issues
\item Good Articles: articles without problems of gaps or excessive content.
\item B-Class: Useful but withoit precise information
\item Start-Class:Incomplete Articles with references for complete information
\item Stub-Class: Draft Articles
\end{itemize}


Further, we find Zeng et al. \citep{hu1} classifying editors to four general categories, namely, administrators, registered users, anonymous users and banned users.On similar but more refined lines,  \citep{Zhu} defined reviewers as peripheral, central and administrators based on the various leadership behaviours observed with respect to wikepedia interactions , such as providing positive feedback, providing negative feedback, directing someone to work on a particular task, and exchanging social information. The paper concluded on the results observed by applying the classification model to approximately 4 million messages between Wikipedia editors. \\

So far, there have been no work to study how increase in collaboration speeds up the process of information accumulation. Using the information collected from the literature, we intend to quantify Knowledge in the article and see the trend from version to version. Based on the various quality measures, the change in quality with an increase in collaboration can also be measured and analyzed. 



%Here is an equation\footnote{the notation is explained in the nomenclature section :-)}:
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%CIF: \hspace*{5mm}F_0^j(a) &=& \frac{1}{2\pi \iota} \oint_{\gamma} \frac{F_0^j(z)}{z - a} dz
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%\nomenclature[zcif]{$CIF$}{Cauchy's Integral Formula}                                % first letter Z is for Acronyms 
%\nomenclature[aF]{$F$}{complex function}                                                   % first letter A is for Roman symbols
%\nomenclature[gp]{$\pi$}{ $\simeq 3.14\ldots$}                                             % first letter G is for Greek Symbols
%\nomenclature[gi]{$\iota$}{unit imaginary number $\sqrt{-1}$}                      % first letter G is for Greek Symbols
%\nomenclature[gg]{$\gamma$}{a simply closed curve on a complex plane}  % first letter G is for Greek Symbols
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